scipy.stats.mode(array, axis=0) function calculates the mode of the array elements along the specified axis of the array (list in python).
Its formula –
where, l : Lower Boundary of modal class h : Size of modal class fm : Frequency corresponding to modal class f1 : Frequency preceding to modal class f2 : Frequency proceeding to modal class
Parameters :
array : Input array or object having the elements to calculate the mode.
axis : Axis along which the mode is to be computed. By default axis = 0Returns : Modal values of the array elements based on the set parameters.
Code #1:
# Arithmetic mode from scipy import statsimport numpy as np arr1 = np.array([[1, 3, 27, 13, 21, 9], [8, 12, 8, 4, 7, 10]]) print("Arithmetic mode is : \n", stats.mode(arr1)) |
Output :
Arithmetic mode is : ModeResult(mode=array([[1, 3, 8, 4, 7, 9]]), count=array([[1, 1, 1, 1, 1, 1]]))
Code #2: With multi-dimensional data
# Arithmetic mode from scipy import statsimport numpy as np arr1 = [[1, 3, 27], [3, 4, 6], [7, 6, 3], [3, 6, 8]] print("Arithmetic mode is : \n", stats.mode(arr1)) print("\nArithmetic mode is : \n", stats.mode(arr1, axis = None)) print("\nArithmetic mode is : \n", stats.mode(arr1, axis = 0)) print("\nArithmetic mode is : \n", stats.mode(arr1, axis = 1)) |
Output :
Arithmetic mode is :
ModeResult(mode=array([[3, 6, 3]]), count=array([[2, 2, 1]]))
Arithmetic mode is :
ModeResult(mode=array([3]), count=array([4]))
Arithmetic mode is :
ModeResult(mode=array([[3, 6, 3]]), count=array([[2, 2, 1]]))
Arithmetic mode is :
ModeResult(mode=array([[1],
[3],
[3],
[3]]), count=array([[1],
[1],
[1],
[1]]))
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